LEADER 05678nam 2200757Ia 450 001 9910139802403321 005 20200520144314.0 010 $a9786612278884 010 $a9781282278882 010 $a1282278886 010 $a9780470749555 010 $a0470749555 010 $a9780470749562 010 $a0470749563 035 $a(CKB)1000000000790823 035 $a(EBL)470176 035 $a(OCoLC)648759664 035 $a(SSID)ssj0000354259 035 $a(PQKBManifestationID)11256487 035 $a(PQKBTitleCode)TC0000354259 035 $a(PQKBWorkID)10313201 035 $a(PQKB)10307526 035 $a(MiAaPQ)EBC470176 035 $a(Au-PeEL)EBL470176 035 $a(CaPaEBR)ebr10333027 035 $a(CaONFJC)MIL227888 035 $a(Perlego)2761146 035 $a(EXLCZ)991000000000790823 100 $a20090728d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGraphical models $emethods for data analysis and mining /$fChristian Borgelt, Matthias Steinbrecher & Rudolf Kruse 205 $a2nd ed. 210 $aHoboken, NJ $cJohn Wiley$dc2009 215 $a1 online resource (405 p.) 225 1 $aWiley series in computational statistics 300 $aDescription based upon print version of record. 311 08$a9780470722107 311 08$a047072210X 320 $aIncludes bibliographical references and index. 327 $aGraphical Models; Contents; Preface; 1 Introduction; 1.1 Data and Knowledge; 1.2 Knowledge Discovery and Data Mining; 1.2.1 The KDD Process; 1.2.2 Data Mining Tasks; 1.2.3 Data Mining Methods; 1.3 Graphical Models; 1.4 Outline of this Book; 2 Imprecision and Uncertainty; 2.1 Modeling Inferences; 2.2 Imprecision and Relational Algebra; 2.3 Uncertainty and Probability Theory; 2.4 Possibility Theory and the Context Model; 2.4.1 Experiments with Dice; 2.4.2 The Context Model; 2.4.3 The Insufficient Reason Principle; 2.4.4 Overlapping Contexts; 2.4.5 Mathematical Formalization 327 $a2.4.6 Normalization and Consistency2.4.7 Possibility Measures; 2.4.8 Mass Assignment Theory; 2.4.9 Degrees of Possibility for Decision Making; 2.4.10 Conditional Degrees of Possibility; 2.4.11 Imprecision and Uncertainty; 2.4.12 Open Problems; 3 Decomposition; 3.1 Decomposition and Reasoning; 3.2 Relational Decomposition; 3.2.1 A Simple Example; 3.2.2 Reasoning in the Simple Example; 3.2.3 Decomposability of Relations; 3.2.4 Tuple-Based Formalization; 3.2.5 Possibility-Based Formalization; 3.2.6 Conditional Possibility and Independence; 3.3 Probabilistic Decomposition; 3.3.1 A Simple Example 327 $a3.3.2 Reasoning in the Simple Example3.3.3 Factorization of Probability Distributions; 3.3.4 Conditional Probability and Independence; 3.4 Possibilistic Decomposition; 3.4.1 Transfer from Relational Decomposition; 3.4.2 A Simple Example; 3.4.3 Reasoning in the Simple Example; 3.4.4 Conditional Degrees of Possibility and Independence; 3.5 Possibility versus Probability; 4 Graphical Representation; 4.1 Conditional Independence Graphs; 4.1.1 Axioms of Conditional Independence; 4.1.2 Graph Terminology; 4.1.3 Separation in Graphs; 4.1.4 Dependence and Independence Maps 327 $a4.1.5 Markov Properties of Graphs4.1.6 Markov Equivalence of Graphs; 4.1.7 Graphs and Decompositions; 4.1.8 Markov Networks and Bayesian Networks; 4.2 Evidence Propagation in Graphs; 4.2.1 Propagation in Undirected Trees; 4.2.2 Join Tree Propagation; 4.2.3 Other Evidence Propagation Methods; 5 Computing Projections; 5.1 Databases of Sample Cases; 5.2 Relational and Sum Projections; 5.3 Expectation Maximization; 5.4 Maximum Projections; 5.4.1 A Simple Example; 5.4.2 Computation via the Support; 5.4.3 Computation via the Closure; 5.4.4 Experimental Evaluation; 5.4.5 Limitations 327 $a6 Naive Classifiers6.1 Naive Bayes Classifiers; 6.1.1 The Basic Formula; 6.1.2 Relation to Bayesian Networks; 6.1.3 A Simple Example; 6.2 A Naive Possibilistic Classifier; 6.3 Classifier Simplification; 6.4 Experimental Evaluation; 7 Learning Global Structure; 7.1 Principles of Learning Global Structure; 7.1.1 Learning Relational Networks; 7.1.2 Learning Probabilistic Networks; 7.1.3 Learning Possibilistic Networks; 7.1.4 Components of a Learning Algorithm; 7.2 Evaluation Measures; 7.2.1 General Considerations; 7.2.2 Notation and Presuppositions; 7.2.3 Relational Evaluation Measures 327 $a7.2.4 Probabilistic Evaluation Measures 330 $aGraphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of dis 410 0$aWiley series in computational statistics. 606 $aData mining 606 $aMathematical statistics$xGraphic methods 615 0$aData mining. 615 0$aMathematical statistics$xGraphic methods. 676 $a006.3/12 700 $aBorgelt$b Christian$0280169 701 $aSteinbrecher$b Matthias$0522159 701 $aKruse$b Rudolf$0102093 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139802403321 996 $aGraphical Models$9835672 997 $aUNINA LEADER 03936nam 2201057z- 450 001 9910557647103321 005 20210501 035 $a(CKB)5400000000044982 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68474 035 $a(oapen)doab68474 035 $a(EXLCZ)995400000000044982 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMathematical Models for the Design of Electrical Machines 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (252 p.) 311 08$a3-0365-0398-6 311 08$a3-0365-0399-4 330 $aThis book is a comprehensive set of articles reflecting the latest advances and developments in mathematical modeling and the design of electrical machines for different applications. The main models discussed are based on the: i) Maxwell-Fourier method (i.e., the formal resolution of Maxwell's equations by using the separation of variables method and the Fourier's series in 2-D or 3-D with a quasi-Cartesian or polar coordinate system); ii) electrical, thermal and magnetic equivalent circuit; iii) hybrid model. In these different papers, the numerical method and the experimental tests have been used as comparisons or validations. 606 $aHistory of engineering and technology$2bicssc 610 $a2D 610 $a2D steady-state models 610 $a3D finite element method 610 $aanalytical expression 610 $aanalytical method 610 $aanalytical model 610 $aanalytical modeling 610 $acomplex harmonic modeling 610 $aeddy-current 610 $aeddy-current losses 610 $aelectric machines 610 $aelectrical machines 610 $aelectromagnetic performances 610 $aexperiment 610 $afinite iron relative permeability 610 $afinite-element analysis 610 $aFourier analysis 610 $ahigh-speed 610 $ahybrid analytical modeling 610 $ahybrid excitation 610 $ahybrid model 610 $alinear induction motors 610 $aloss reduction 610 $amagnet shape optimization 610 $amagnetic equivalent circuit 610 $amagnetic equivalent circuits 610 $aMaxwell-Fourier method 610 $amulti-phase 610 $amultiphase induction machine 610 $an/a 610 $anodal method 610 $anon-homogeneous permeability 610 $anumerical 610 $apartial differential equations 610 $apermanent magnet machines 610 $apermanent magnet motor 610 $apermanent-magnet 610 $apermanent-magnet losses 610 $areduced order 610 $arotating machines 610 $arotor cage 610 $ascattering matrix 610 $asegmentation 610 $aseparation of variable technique 610 $asinusoidal current excitation 610 $asleeve 610 $asubdomain technique 610 $asurface inset permanent magnet 610 $asurface-mounted PM machines 610 $aswitched reluctance machine 610 $asynchronous machines 610 $athermal analysis 610 $athermal equivalence circuit 610 $athermal resistances 610 $atorque pulsation 610 $atorque pulsations 610 $aVoronoi? tessellation 610 $awinding heads 615 7$aHistory of engineering and technology 700 $aDubas$b Fre?de?ric$4edt$00 702 $aBoughrara$b Kamel$4edt 702 $aDubas$b Fre?de?ric$4oth 702 $aBoughrara$b Kamel$4oth 906 $aBOOK 912 $a9910557647103321 996 $aMathematical Models for the Design of Electrical Machines$93035732 997 $aUNINA